The Search for Suitable Habitats for Endangered Species at Their Historical Sites-Conditions for the Success of Salix lapponum and Salix myrtilloides Reintroduction.

Restoring endangered plant species to their historical sites is not always possible due to constantly changing habitat conditions. The aim of this study was to test the effects of reintroduction of two relict willow species in eastern Poland. The experiment consisted of planting 48 individuals of Salix lapponum and S. myrtilloides, obtained by micropropagation, at each of the two selected sites and observing their survival after one year. At the same time, selected physicochemical and biocenotic factors of the environment were monitored. About 70% of S. lapponum individuals and 50% of S. myrtilloides plants survived the one-year period. This result can be considered satisfactory and confirms the effectiveness of this means of active protection. The results of measurements of selected abiotic factors of the environment and the observations and ecological analysis of the flora indicated that the habitat conditions of both historical sites have changed, resulting in accelerated succession of vegetation. However, complete habitat degradation did not occur, although the development of a multi-story structure of one of the phytocenoses intensified competition for light and other environmental resources, which narrowed the potential ecological niche of the reintroduced species.

Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors.

Reliable tools for artefact rejection and signal classification are a must for cosmic ray detection experiments based on CMOS technology. In this paper, we analyse the fitness of several feature-based statistical classifiers for the classification of particle candidate hits in four categories: spots, tracks, worms and artefacts. We use Zernike moments of the image function as feature carriers and propose a preprocessing and denoising scheme to make the feature extraction more efficient. As opposed to convolution neural network classifiers, the feature-based classifiers allow for establishing a connection between features and geometrical properties of candidate hits. Apart from basic classifiers we also consider their ensemble extensions and find these extensions generally better performing than basic versions, with an average recognition accuracy of 88%.

CNN-Based Classifier as an Offline Trigger for the CREDO Experiment.

Gamification is known to enhance users' participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process.

Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

Effects of rotifers, copepods and chironomid larvae on microbial communities in peatlands.

Interactions between the microbial loop and the classical grazing food chain are essential to ecosystem ecology. The goal of the present study was to examine the impact of chironomid larvae, rotifers and copepods on the major components of the microbial food web (algae, bacteria, heterotrophic flagellates, testate amoebae and ciliates) in peatlands. Two enclosure experiments were carried out in a Sphagnum peatland. In the experiments we manipulated rotifers, copepods and macroinvertebrates, i.e. chironomid larvae (Psectrocladius sordidellus gr). During the experiments variation was observed in the abundance of potential predators. The beginning of the first experiment was distinguished by dominance of rotifers, but five days later copepods were dominant. In the second experiment copepods dominated. The results of this study are the first to suggest a substantial impact of chironomid larvae, rotifers and copepods on microorganism communities in peatland ecosystems. The impact is reflected by both a decrease in the abundance and biomass of testate amoebae and ciliates and a transformation of the size structure of bacteria. Heterotrophic flagellates (HNF) were not controlled by metazoans, but rather by testate amoebae and ciliates, as HNF were more abundant in the control treatment.